In collaborative robotic cells, a human operator and a robot share the workspace in order to execute a common job, consisting of a set of tasks. A proper allocation and scheduling of the tasks for the human and for the robot is crucial for achieving an efficient human-robot collaboration. In order to deal with the dynamic and unpredictable behavior of the human and for allowing the human and the robot to negotiate about the tasks to be executed, a two layers architecture for solving the task allocation and scheduling problem is proposed. The first layer optimally solves the task allocation problem considering nominal execution times. The second layer, which is reactive, adapts online the sequence of tasks to be executed by the robot considering deviations from the nominal behaviors and requests coming from the human and from robot. The proposed architecture is experimentally validated on a collaborative assembly job.


    Access

    Download


    Export, share and cite



    Title :

    A Dynamic Architecture for Task Assignment and Scheduling for Collaborative Robotic Cells


    Contributors:

    Publication date :

    2021-04-29



    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    Classification :

    DDC:    629




    A @ Dynamic Architecture for Task Assignment and Scheduling for Collaborative Robotic Cells

    Pupa, Andrea / Landi, Chiara Talignani / Bertolani, Mattia et al. | TIBKAT | 2021


    In-Space Crew-Collaborative Task Scheduling

    Jaap, J. / Meyer, P. / Davis, E. et al. | British Library Conference Proceedings | 2007


    In-Space Crew-Collaborative Task Scheduling

    Jaap, John / Meyer, Patrick / Davis, Elizabeth et al. | AIAA | 2006


    In-Space Crew-Collaborative Task Scheduling

    Douglas, J. Paul / Bruca, Loredana / Sorensen, Trevor | AIAA | 2007


    A Human-Centered Dynamic Scheduling Architecture for Collaborative Application

    Pupa, Andrea / Van Dijk, Wietse / Secchi, Cristian | BASE | 2021

    Free access